Recently, models that understand programming languages (PL) have been studied, and show promising results in diverse code comprehension tasks [1]}, [2]}, [3]}.
However, these models have limitations to support question answering.
They are not trained on datasets containing questions about the code and are not designed for QA tasks. Also, many assume fully functional code as input, while students' code contains diverse syntax and logical errors and is often incomplete.
